Hyperspectral Image Processing for Automatic Target Detection Applications

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Hyperspectral Imaging: A Review on UAV-Based Sensors, Data Processing and Applications for Agriculture and Forestry (2017, methodology): “To that end, Manolakis and Shaw [71] underline the importance of the constant false alarm rate (CFAR) processor for dealing with misclassifications.”
A Low-Rank and Sparse Matrix Decomposition-Based Mahalanobis Distance Method for Hyperspectral Anomaly Detection (2016, background): “alleviating covariance contamination caused by anomalies [8], [9].”
A Novel Ranking-Based Clustering Approach for Hyperspectral Band Selection (2016, background): “Clearly, the obtained hundreds of images can form a 3-D cube, which can be used for surface material classification and identification according to the continuous spectrum of each pixel [1]–[4].”
Saliency and Gist Features for Target Detection in Satellite Images (2011, background): “For example, for hyper-spectral satellite images, the features applied usually take advantage of the reﬂection characteristics of different materials [20]–[22] while for multispectral images, the features are usually extracted from fused spectra [23], [24].”